[ECCV 2024] Official implementation for "RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception"
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Updated
Jul 16, 2024
[ECCV 2024] Official implementation for "RepVF: A Unified Vector Fields Representation for Multi-task 3D Perception"
Master Thesis: Weakly Supervised Monocular 3D Object Detection
nnDetection is a self-configuring framework for 3D (volumetric) medical object detection which can be applied to new data sets without manual intervention. It includes guides for 12 data sets that were used to develop and evaluate the performance of the proposed method.
[ICLR2024] HEAL: An Extensible Framework for Open Heterogeneous Collaborative Perception ➡️ All You Need for Multi-Modality Collaborative Perception!
OpenMMLab's next-generation platform for general 3D object detection.
A Cascade Attention Network for 3D Object Detection from LiDAR point clouds
[ECCV 2024] RecurrentBEV: A Long-term Temporal Fusion Framework for Multi-view 3D Detection
[ECCV 2024] 3D Small Object Detection with Dynamic Spatial Pruning
[ECCV 2024] Ray Denoising (RayDN): Depth-aware Hard Negative Sampling for Multi-view 3D Object Detection
[CVPR2023] Official Implementation of "DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets"
[ICCV2023] Official Implementation of "UniTR: A Unified and Efficient Multi-Modal Transformer for Bird’s-Eye-View Representation"
[ECCV2024] This is the official implementation of GraphBEV, a BEV multi-modal framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.
YOLO 3D Object Detection for Autonomous Driving Vehicle
Advanced 3D Multi Object Tracking using Multi Model Kalman Filter using Long Range and Short Range Tendency for Autonomous Vehicle
Ultralytics YOLOv8, YOLOv9, YOLOv10 for ROS 2
[IVS'24] UniBEV: the official implementation of UniBEV
[CVPR 2024] Memory-based Adapters for Online 3D Scene Perception
Paper reading notes on Deep Learning and Machine Learning
[CVPR 2024] Official PyTorch Code of SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks [IJCV2023]
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